Run Time Approximation of Non-blocking Service Rates for Streaming Systems

Stream processing is a compute paradigm that promises safe and efficient parallelism. Modern big-data problems are often well suited for stream processing's throughput-oriented nature. Realization of efficient stream processing requires monitoring and optimization of multiple communications links. Most techniques to optimize these links use queueing network models or network flow models, which require some idea of the actual execution rate of each independent compute kernel within the system. What we want to know is how fast can each kernel process data independent of other communicating kernels. This is known as the "service rate" of the kernel within the queueing literature. Current approaches to divining service rates are static. Modern workloads, however, are often dynamic. Shared cloud systems also present applications with highly dynamic execution environments (multiple users, hardware migration, etc.). It is therefore desirable to continuously re-tune an application during run time (online) in response to changing conditions. Our approach enables online service rate monitoring under most conditions, obviating the need for reliance on steady state predictions for what are probably non-steady state phenomena. First, some of the difficulties associated with online service rate determination are examined. Second, the algorithm to approximate the online non-blocking service rate is described. Lastly, the algorithm is implemented within the open source RaftLib framework for validation using a simple microbenchmark as well as two full streaming applications.Comment: technical repor

Analysis of data processing systems

Mathematical simulation models and software monitoring of multiprogramming computer syste

MGSim - Simulation tools for multi-core processor architectures

MGSim is an open source discrete event simulator for on-chip hardware components, developed at the University of Amsterdam. It is intended to be a research and teaching vehicle to study the fine-grained hardware/software interactions on many-core and hardware multithreaded processors. It includes support for core models with different instruction sets, a configurable multi-core interconnect, multiple configurable cache and memory models, a dedicated I/O subsystem, and comprehensive monitoring and interaction facilities. The default model configuration shipped with MGSim implements Microgrids, a many-core architecture with hardware concurrency management. MGSim is furthermore written mostly in C++ and uses object classes to represent chip components. It is optimized for architecture models that can be described as process networks.Comment: 33 pages, 22 figures, 4 listings, 2 table

Standard Testing Procedure for Quantifying Breathing Gas Carbon Dioxide Partial Pressure for Extravehicular Activity and Launch, Entry, Survival Pressure Suits

This standard test and analysis protocol establishes the procedure for determining the partial pressure of inspired carbon dioxide (PICO2) exposure level experienced by persons operating a pressurized suit. The purpose of this Standard Testing Procedure (STP) is to describe the test conditions and procedures necessary to acquire data in support of certification that manufacturer submitted Extravehicular Activity (EVA) and/or Launch, Entry, Survival (LES) suit designs maintain safe levels of carbon dioxide (CO2) in the helmet during suited operations. The STP shall be used to measure the in-suit inhaled and exhaled dry-gas partial pressure of CO2 (PCO2), followed by calculation of the water vapor saturated PICO2 during the inhalation portion of the breathing cycle, while a human test subject is performing work at levels anticipated during suited operations in ground and flight environments. The procedure is designed to test the evaluated suit on a human test subject as a dynamic system, generate repeatable results under defined laboratory conditions, and perform consistent analysis on acquired samples.This STP is used to evaluate space suits in a hyperbaric environment (above atmospheric pressure). Changes would need to be made to the test equipment/setup to accommodate a hypobaric environment. There is no specific EVA or LES suit performance requirement to meet or pass/fail criteria associated with this STP

Unsupervised Anomaly-based Malware Detection using Hardware Features

Recent works have shown promise in using microarchitectural execution patterns to detect malware programs. These detectors belong to a class of detectors known as signature-based detectors as they catch malware by comparing a program's execution pattern (signature) to execution patterns of known malware programs. In this work, we propose a new class of detectors - anomaly-based hardware malware detectors - that do not require signatures for malware detection, and thus can catch a wider range of malware including potentially novel ones. We use unsupervised machine learning to build profiles of normal program execution based on data from performance counters, and use these profiles to detect significant deviations in program behavior that occur as a result of malware exploitation. We show that real-world exploitation of popular programs such as IE and Adobe PDF Reader on a Windows/x86 platform can be detected with nearly perfect certainty. We also examine the limits and challenges in implementing this approach in face of a sophisticated adversary attempting to evade anomaly-based detection. The proposed detector is complementary to previously proposed signature-based detectors and can be used together to improve security.Comment: 1 page, Latex; added description for feature selection in Section 4, results unchange